Merge 405d0e8001 into sapling-pr-archive-ehhuang

client-sdks/stainless/openapi.yml

@@ -350,146 +350,46 @@ paths:
           in: query
           description: >-
             An item ID to list items after, used in pagination.
-          required: true
+          required: false
           schema:
-            oneOf:
+            type: string
-              - type: string
-              - type: object
-                title: NotGiven
-                description: >-
-                  A sentinel singleton class used to distinguish omitted keyword arguments
-                  from those passed in with the value None (which may have different
-                  behavior).
-
-                  For example:
-
-
-                  ```py
-
-                  def get(timeout: Union[int, NotGiven, None] = NotGiven()) -> Response:
-                  ...
-
-
-
-                  get(timeout=1)  # 1s timeout
-
-                  get(timeout=None)  # No timeout
-
-                  get()  # Default timeout behavior, which may not be statically known
-                  at the method definition.
-
-                  ```
         - name: include
           in: query
           description: >-
             Specify additional output data to include in the response.
-          required: true
+          required: false
           schema:
-            oneOf:
+            type: array
-              - type: array
             items:
               type: string
               enum:
+                - web_search_call.action.sources
                 - code_interpreter_call.outputs
                 - computer_call_output.output.image_url
                 - file_search_call.results
                 - message.input_image.image_url
                 - message.output_text.logprobs
                 - reasoning.encrypted_content
-              - type: object
+              title: ConversationItemInclude
-                title: NotGiven
               description: >-
-                  A sentinel singleton class used to distinguish omitted keyword arguments
+                Specify additional output data to include in the model response.
-                  from those passed in with the value None (which may have different
-                  behavior).
-
-                  For example:
-
-
-                  ```py
-
-                  def get(timeout: Union[int, NotGiven, None] = NotGiven()) -> Response:
-                  ...
-
-
-
-                  get(timeout=1)  # 1s timeout
-
-                  get(timeout=None)  # No timeout
-
-                  get()  # Default timeout behavior, which may not be statically known
-                  at the method definition.
-
-                  ```
         - name: limit
           in: query
           description: >-
             A limit on the number of objects to be returned (1-100, default 20).
-          required: true
+          required: false
           schema:
-            oneOf:
+            type: integer
-              - type: integer
-              - type: object
-                title: NotGiven
-                description: >-
-                  A sentinel singleton class used to distinguish omitted keyword arguments
-                  from those passed in with the value None (which may have different
-                  behavior).
-
-                  For example:
-
-
-                  ```py
-
-                  def get(timeout: Union[int, NotGiven, None] = NotGiven()) -> Response:
-                  ...
-
-
-
-                  get(timeout=1)  # 1s timeout
-
-                  get(timeout=None)  # No timeout
-
-                  get()  # Default timeout behavior, which may not be statically known
-                  at the method definition.
-
-                  ```
         - name: order
           in: query
           description: >-
             The order to return items in (asc or desc, default desc).
-          required: true
+          required: false
           schema:
-            oneOf:
+            type: string
-              - type: string
             enum:
               - asc
               - desc
-              - type: object
-                title: NotGiven
-                description: >-
-                  A sentinel singleton class used to distinguish omitted keyword arguments
-                  from those passed in with the value None (which may have different
-                  behavior).
-
-                  For example:
-
-
-                  ```py
-
-                  def get(timeout: Union[int, NotGiven, None] = NotGiven()) -> Response:
-                  ...
-
-
-
-                  get(timeout=1)  # 1s timeout
-
-                  get(timeout=None)  # No timeout
-
-                  get()  # Default timeout behavior, which may not be statically known
-                  at the method definition.
-
-                  ```
       deprecated: false
     post:
       responses:
@@ -6482,6 +6382,7 @@ components:
       enum:
         - llm
         - embedding
+        - rerank
       title: ModelType
       description: >-
         Enumeration of supported model types in Llama Stack.
@@ -13585,13 +13486,16 @@ tags:
       embeddings.
 
 
-      This API provides the raw interface to the underlying models. Two kinds of models
+      This API provides the raw interface to the underlying models. Three kinds of
-      are supported:
+      models are supported:
 
       - LLM models: these models generate "raw" and "chat" (conversational) completions.
 
       - Embedding models: these models generate embeddings to be used for semantic
       search.
+
+      - Rerank models: these models reorder the documents based on their relevance
+      to a query.
     x-displayName: Inference
   - name: Inspect
     description: >-

docs/docs/providers/inference/index.mdx

@@ -3,9 +3,10 @@ description: "Inference
 
     Llama Stack Inference API for generating completions, chat completions, and embeddings.
 
-    This API provides the raw interface to the underlying models. Two kinds of models are supported:
+    This API provides the raw interface to the underlying models. Three kinds of models are supported:
     - LLM models: these models generate \"raw\" and \"chat\" (conversational) completions.
-    - Embedding models: these models generate embeddings to be used for semantic search."
+    - Embedding models: these models generate embeddings to be used for semantic search.
+    - Rerank models: these models reorder the documents based on their relevance to a query."
 sidebar_label: Inference
 title: Inference
 ---
@@ -18,8 +19,9 @@ Inference
 
     Llama Stack Inference API for generating completions, chat completions, and embeddings.
 
-    This API provides the raw interface to the underlying models. Two kinds of models are supported:
+    This API provides the raw interface to the underlying models. Three kinds of models are supported:
     - LLM models: these models generate "raw" and "chat" (conversational) completions.
     - Embedding models: these models generate embeddings to be used for semantic search.
+    - Rerank models: these models reorder the documents based on their relevance to a query.
 
 This section contains documentation for all available providers for the **inference** API.

docs/static/deprecated-llama-stack-spec.html

@@ -13467,7 +13467,7 @@
         },
         {
             "name": "Inference",
-            "description": "Llama Stack Inference API for generating completions, chat completions, and embeddings.\n\nThis API provides the raw interface to the underlying models. Two kinds of models are supported:\n- LLM models: these models generate \"raw\" and \"chat\" (conversational) completions.\n- Embedding models: these models generate embeddings to be used for semantic search.",
+            "description": "Llama Stack Inference API for generating completions, chat completions, and embeddings.\n\nThis API provides the raw interface to the underlying models. Three kinds of models are supported:\n- LLM models: these models generate \"raw\" and \"chat\" (conversational) completions.\n- Embedding models: these models generate embeddings to be used for semantic search.\n- Rerank models: these models reorder the documents based on their relevance to a query.",
             "x-displayName": "Inference"
         },
         {

docs/static/deprecated-llama-stack-spec.yaml

@@ -10218,13 +10218,16 @@ tags:
       embeddings.
 
 
-      This API provides the raw interface to the underlying models. Two kinds of models
+      This API provides the raw interface to the underlying models. Three kinds of
-      are supported:
+      models are supported:
 
       - LLM models: these models generate "raw" and "chat" (conversational) completions.
 
       - Embedding models: these models generate embeddings to be used for semantic
       search.
+
+      - Rerank models: these models reorder the documents based on their relevance
+      to a query.
     x-displayName: Inference
   - name: Models
     description: ''

docs/static/llama-stack-spec.html

@@ -483,87 +483,54 @@
                         "name": "after",
                         "in": "query",
                         "description": "An item ID to list items after, used in pagination.",
-                        "required": true,
+                        "required": false,
                         "schema": {
-                            "oneOf": [
-                                {
                             "type": "string"
-                                },
-                                {
-                                    "type": "object",
-                                    "title": "NotGiven",
-                                    "description": "A sentinel singleton class used to distinguish omitted keyword arguments from those passed in with the value None (which may have different behavior).\nFor example:\n\n```py\ndef get(timeout: Union[int, NotGiven, None] = NotGiven()) -> Response: ...\n\n\nget(timeout=1)  # 1s timeout\nget(timeout=None)  # No timeout\nget()  # Default timeout behavior, which may not be statically known at the method definition.\n```"
-                                }
-                            ]
                         }
                     },
                     {
                         "name": "include",
                         "in": "query",
                         "description": "Specify additional output data to include in the response.",
-                        "required": true,
+                        "required": false,
                         "schema": {
-                            "oneOf": [
-                                {
                             "type": "array",
                             "items": {
                                 "type": "string",
                                 "enum": [
+                                    "web_search_call.action.sources",
                                     "code_interpreter_call.outputs",
                                     "computer_call_output.output.image_url",
                                     "file_search_call.results",
                                     "message.input_image.image_url",
                                     "message.output_text.logprobs",
                                     "reasoning.encrypted_content"
-                                        ]
+                                ],
+                                "title": "ConversationItemInclude",
+                                "description": "Specify additional output data to include in the model response."
                             }
-                                },
-                                {
-                                    "type": "object",
-                                    "title": "NotGiven",
-                                    "description": "A sentinel singleton class used to distinguish omitted keyword arguments from those passed in with the value None (which may have different behavior).\nFor example:\n\n```py\ndef get(timeout: Union[int, NotGiven, None] = NotGiven()) -> Response: ...\n\n\nget(timeout=1)  # 1s timeout\nget(timeout=None)  # No timeout\nget()  # Default timeout behavior, which may not be statically known at the method definition.\n```"
-                                }
-                            ]
                         }
                     },
                     {
                         "name": "limit",
                         "in": "query",
                         "description": "A limit on the number of objects to be returned (1-100, default 20).",
-                        "required": true,
+                        "required": false,
                         "schema": {
-                            "oneOf": [
-                                {
                             "type": "integer"
-                                },
-                                {
-                                    "type": "object",
-                                    "title": "NotGiven",
-                                    "description": "A sentinel singleton class used to distinguish omitted keyword arguments from those passed in with the value None (which may have different behavior).\nFor example:\n\n```py\ndef get(timeout: Union[int, NotGiven, None] = NotGiven()) -> Response: ...\n\n\nget(timeout=1)  # 1s timeout\nget(timeout=None)  # No timeout\nget()  # Default timeout behavior, which may not be statically known at the method definition.\n```"
-                                }
-                            ]
                         }
                     },
                     {
                         "name": "order",
                         "in": "query",
                         "description": "The order to return items in (asc or desc, default desc).",
-                        "required": true,
+                        "required": false,
                         "schema": {
-                            "oneOf": [
-                                {
                             "type": "string",
                             "enum": [
                                 "asc",
                                 "desc"
                             ]
-                                },
-                                {
-                                    "type": "object",
-                                    "title": "NotGiven",
-                                    "description": "A sentinel singleton class used to distinguish omitted keyword arguments from those passed in with the value None (which may have different behavior).\nFor example:\n\n```py\ndef get(timeout: Union[int, NotGiven, None] = NotGiven()) -> Response: ...\n\n\nget(timeout=1)  # 1s timeout\nget(timeout=None)  # No timeout\nget()  # Default timeout behavior, which may not be statically known at the method definition.\n```"
-                                }
-                            ]
                         }
                     }
                 ],
@@ -6859,7 +6826,8 @@
                 "type": "string",
                 "enum": [
                     "llm",
-                    "embedding"
+                    "embedding",
+                    "rerank"
                 ],
                 "title": "ModelType",
                 "description": "Enumeration of supported model types in Llama Stack."
@@ -13269,7 +13237,7 @@
         },
         {
             "name": "Inference",
-            "description": "Llama Stack Inference API for generating completions, chat completions, and embeddings.\n\nThis API provides the raw interface to the underlying models. Two kinds of models are supported:\n- LLM models: these models generate \"raw\" and \"chat\" (conversational) completions.\n- Embedding models: these models generate embeddings to be used for semantic search.",
+            "description": "Llama Stack Inference API for generating completions, chat completions, and embeddings.\n\nThis API provides the raw interface to the underlying models. Three kinds of models are supported:\n- LLM models: these models generate \"raw\" and \"chat\" (conversational) completions.\n- Embedding models: these models generate embeddings to be used for semantic search.\n- Rerank models: these models reorder the documents based on their relevance to a query.",
             "x-displayName": "Inference"
         },
         {

docs/static/llama-stack-spec.yaml

@@ -347,146 +347,46 @@ paths:
           in: query
           description: >-
             An item ID to list items after, used in pagination.
-          required: true
+          required: false
           schema:
-            oneOf:
+            type: string
-              - type: string
-              - type: object
-                title: NotGiven
-                description: >-
-                  A sentinel singleton class used to distinguish omitted keyword arguments
-                  from those passed in with the value None (which may have different
-                  behavior).
-
-                  For example:
-
-
-                  ```py
-
-                  def get(timeout: Union[int, NotGiven, None] = NotGiven()) -> Response:
-                  ...
-
-
-
-                  get(timeout=1)  # 1s timeout
-
-                  get(timeout=None)  # No timeout
-
-                  get()  # Default timeout behavior, which may not be statically known
-                  at the method definition.
-
-                  ```
         - name: include
           in: query
           description: >-
             Specify additional output data to include in the response.
-          required: true
+          required: false
           schema:
-            oneOf:
+            type: array
-              - type: array
             items:
               type: string
               enum:
+                - web_search_call.action.sources
                 - code_interpreter_call.outputs
                 - computer_call_output.output.image_url
                 - file_search_call.results
                 - message.input_image.image_url
                 - message.output_text.logprobs
                 - reasoning.encrypted_content
-              - type: object
+              title: ConversationItemInclude
-                title: NotGiven
               description: >-
-                  A sentinel singleton class used to distinguish omitted keyword arguments
+                Specify additional output data to include in the model response.
-                  from those passed in with the value None (which may have different
-                  behavior).
-
-                  For example:
-
-
-                  ```py
-
-                  def get(timeout: Union[int, NotGiven, None] = NotGiven()) -> Response:
-                  ...
-
-
-
-                  get(timeout=1)  # 1s timeout
-
-                  get(timeout=None)  # No timeout
-
-                  get()  # Default timeout behavior, which may not be statically known
-                  at the method definition.
-
-                  ```
         - name: limit
           in: query
           description: >-
             A limit on the number of objects to be returned (1-100, default 20).
-          required: true
+          required: false
           schema:
-            oneOf:
+            type: integer
-              - type: integer
-              - type: object
-                title: NotGiven
-                description: >-
-                  A sentinel singleton class used to distinguish omitted keyword arguments
-                  from those passed in with the value None (which may have different
-                  behavior).
-
-                  For example:
-
-
-                  ```py
-
-                  def get(timeout: Union[int, NotGiven, None] = NotGiven()) -> Response:
-                  ...
-
-
-
-                  get(timeout=1)  # 1s timeout
-
-                  get(timeout=None)  # No timeout
-
-                  get()  # Default timeout behavior, which may not be statically known
-                  at the method definition.
-
-                  ```
         - name: order
           in: query
           description: >-
             The order to return items in (asc or desc, default desc).
-          required: true
+          required: false
           schema:
-            oneOf:
+            type: string
-              - type: string
             enum:
               - asc
               - desc
-              - type: object
-                title: NotGiven
-                description: >-
-                  A sentinel singleton class used to distinguish omitted keyword arguments
-                  from those passed in with the value None (which may have different
-                  behavior).
-
-                  For example:
-
-
-                  ```py
-
-                  def get(timeout: Union[int, NotGiven, None] = NotGiven()) -> Response:
-                  ...
-
-
-
-                  get(timeout=1)  # 1s timeout
-
-                  get(timeout=None)  # No timeout
-
-                  get()  # Default timeout behavior, which may not be statically known
-                  at the method definition.
-
-                  ```
       deprecated: false
     post:
       responses:
@@ -5269,6 +5169,7 @@ components:
       enum:
         - llm
         - embedding
+        - rerank
       title: ModelType
       description: >-
         Enumeration of supported model types in Llama Stack.
@@ -10190,13 +10091,16 @@ tags:
       embeddings.
 
 
-      This API provides the raw interface to the underlying models. Two kinds of models
+      This API provides the raw interface to the underlying models. Three kinds of
-      are supported:
+      models are supported:
 
       - LLM models: these models generate "raw" and "chat" (conversational) completions.
 
       - Embedding models: these models generate embeddings to be used for semantic
       search.
+
+      - Rerank models: these models reorder the documents based on their relevance
+      to a query.
     x-displayName: Inference
   - name: Inspect
     description: >-

docs/static/stainless-llama-stack-spec.html

@@ -483,87 +483,54 @@
                         "name": "after",
                         "in": "query",
                         "description": "An item ID to list items after, used in pagination.",
-                        "required": true,
+                        "required": false,
                         "schema": {
-                            "oneOf": [
-                                {
                             "type": "string"
-                                },
-                                {
-                                    "type": "object",
-                                    "title": "NotGiven",
-                                    "description": "A sentinel singleton class used to distinguish omitted keyword arguments from those passed in with the value None (which may have different behavior).\nFor example:\n\n```py\ndef get(timeout: Union[int, NotGiven, None] = NotGiven()) -> Response: ...\n\n\nget(timeout=1)  # 1s timeout\nget(timeout=None)  # No timeout\nget()  # Default timeout behavior, which may not be statically known at the method definition.\n```"
-                                }
-                            ]
                         }
                     },
                     {
                         "name": "include",
                         "in": "query",
                         "description": "Specify additional output data to include in the response.",
-                        "required": true,
+                        "required": false,
                         "schema": {
-                            "oneOf": [
-                                {
                             "type": "array",
                             "items": {
                                 "type": "string",
                                 "enum": [
+                                    "web_search_call.action.sources",
                                     "code_interpreter_call.outputs",
                                     "computer_call_output.output.image_url",
                                     "file_search_call.results",
                                     "message.input_image.image_url",
                                     "message.output_text.logprobs",
                                     "reasoning.encrypted_content"
-                                        ]
+                                ],
+                                "title": "ConversationItemInclude",
+                                "description": "Specify additional output data to include in the model response."
                             }
-                                },
-                                {
-                                    "type": "object",
-                                    "title": "NotGiven",
-                                    "description": "A sentinel singleton class used to distinguish omitted keyword arguments from those passed in with the value None (which may have different behavior).\nFor example:\n\n```py\ndef get(timeout: Union[int, NotGiven, None] = NotGiven()) -> Response: ...\n\n\nget(timeout=1)  # 1s timeout\nget(timeout=None)  # No timeout\nget()  # Default timeout behavior, which may not be statically known at the method definition.\n```"
-                                }
-                            ]
                         }
                     },
                     {
                         "name": "limit",
                         "in": "query",
                         "description": "A limit on the number of objects to be returned (1-100, default 20).",
-                        "required": true,
+                        "required": false,
                         "schema": {
-                            "oneOf": [
-                                {
                             "type": "integer"
-                                },
-                                {
-                                    "type": "object",
-                                    "title": "NotGiven",
-                                    "description": "A sentinel singleton class used to distinguish omitted keyword arguments from those passed in with the value None (which may have different behavior).\nFor example:\n\n```py\ndef get(timeout: Union[int, NotGiven, None] = NotGiven()) -> Response: ...\n\n\nget(timeout=1)  # 1s timeout\nget(timeout=None)  # No timeout\nget()  # Default timeout behavior, which may not be statically known at the method definition.\n```"
-                                }
-                            ]
                         }
                     },
                     {
                         "name": "order",
                         "in": "query",
                         "description": "The order to return items in (asc or desc, default desc).",
-                        "required": true,
+                        "required": false,
                         "schema": {
-                            "oneOf": [
-                                {
                             "type": "string",
                             "enum": [
                                 "asc",
                                 "desc"
                             ]
-                                },
-                                {
-                                    "type": "object",
-                                    "title": "NotGiven",
-                                    "description": "A sentinel singleton class used to distinguish omitted keyword arguments from those passed in with the value None (which may have different behavior).\nFor example:\n\n```py\ndef get(timeout: Union[int, NotGiven, None] = NotGiven()) -> Response: ...\n\n\nget(timeout=1)  # 1s timeout\nget(timeout=None)  # No timeout\nget()  # Default timeout behavior, which may not be statically known at the method definition.\n```"
-                                }
-                            ]
                         }
                     }
                 ],
@@ -8531,7 +8498,8 @@
                 "type": "string",
                 "enum": [
                     "llm",
-                    "embedding"
+                    "embedding",
+                    "rerank"
                 ],
                 "title": "ModelType",
                 "description": "Enumeration of supported model types in Llama Stack."
@@ -17959,7 +17927,7 @@
         },
         {
             "name": "Inference",
-            "description": "Llama Stack Inference API for generating completions, chat completions, and embeddings.\n\nThis API provides the raw interface to the underlying models. Two kinds of models are supported:\n- LLM models: these models generate \"raw\" and \"chat\" (conversational) completions.\n- Embedding models: these models generate embeddings to be used for semantic search.",
+            "description": "Llama Stack Inference API for generating completions, chat completions, and embeddings.\n\nThis API provides the raw interface to the underlying models. Three kinds of models are supported:\n- LLM models: these models generate \"raw\" and \"chat\" (conversational) completions.\n- Embedding models: these models generate embeddings to be used for semantic search.\n- Rerank models: these models reorder the documents based on their relevance to a query.",
             "x-displayName": "Inference"
         },
         {

docs/static/stainless-llama-stack-spec.yaml

@@ -350,146 +350,46 @@ paths:
           in: query
           description: >-
             An item ID to list items after, used in pagination.
-          required: true
+          required: false
           schema:
-            oneOf:
+            type: string
-              - type: string
-              - type: object
-                title: NotGiven
-                description: >-
-                  A sentinel singleton class used to distinguish omitted keyword arguments
-                  from those passed in with the value None (which may have different
-                  behavior).
-
-                  For example:
-
-
-                  ```py
-
-                  def get(timeout: Union[int, NotGiven, None] = NotGiven()) -> Response:
-                  ...
-
-
-
-                  get(timeout=1)  # 1s timeout
-
-                  get(timeout=None)  # No timeout
-
-                  get()  # Default timeout behavior, which may not be statically known
-                  at the method definition.
-
-                  ```
         - name: include
           in: query
           description: >-
             Specify additional output data to include in the response.
-          required: true
+          required: false
           schema:
-            oneOf:
+            type: array
-              - type: array
             items:
               type: string
               enum:
+                - web_search_call.action.sources
                 - code_interpreter_call.outputs
                 - computer_call_output.output.image_url
                 - file_search_call.results
                 - message.input_image.image_url
                 - message.output_text.logprobs
                 - reasoning.encrypted_content
-              - type: object
+              title: ConversationItemInclude
-                title: NotGiven
               description: >-
-                  A sentinel singleton class used to distinguish omitted keyword arguments
+                Specify additional output data to include in the model response.
-                  from those passed in with the value None (which may have different
-                  behavior).
-
-                  For example:
-
-
-                  ```py
-
-                  def get(timeout: Union[int, NotGiven, None] = NotGiven()) -> Response:
-                  ...
-
-
-
-                  get(timeout=1)  # 1s timeout
-
-                  get(timeout=None)  # No timeout
-
-                  get()  # Default timeout behavior, which may not be statically known
-                  at the method definition.
-
-                  ```
         - name: limit
           in: query
           description: >-
             A limit on the number of objects to be returned (1-100, default 20).
-          required: true
+          required: false
           schema:
-            oneOf:
+            type: integer
-              - type: integer
-              - type: object
-                title: NotGiven
-                description: >-
-                  A sentinel singleton class used to distinguish omitted keyword arguments
-                  from those passed in with the value None (which may have different
-                  behavior).
-
-                  For example:
-
-
-                  ```py
-
-                  def get(timeout: Union[int, NotGiven, None] = NotGiven()) -> Response:
-                  ...
-
-
-
-                  get(timeout=1)  # 1s timeout
-
-                  get(timeout=None)  # No timeout
-
-                  get()  # Default timeout behavior, which may not be statically known
-                  at the method definition.
-
-                  ```
         - name: order
           in: query
           description: >-
             The order to return items in (asc or desc, default desc).
-          required: true
+          required: false
           schema:
-            oneOf:
+            type: string
-              - type: string
             enum:
               - asc
               - desc
-              - type: object
-                title: NotGiven
-                description: >-
-                  A sentinel singleton class used to distinguish omitted keyword arguments
-                  from those passed in with the value None (which may have different
-                  behavior).
-
-                  For example:
-
-
-                  ```py
-
-                  def get(timeout: Union[int, NotGiven, None] = NotGiven()) -> Response:
-                  ...
-
-
-
-                  get(timeout=1)  # 1s timeout
-
-                  get(timeout=None)  # No timeout
-
-                  get()  # Default timeout behavior, which may not be statically known
-                  at the method definition.
-
-                  ```
       deprecated: false
     post:
       responses:
@@ -6482,6 +6382,7 @@ components:
       enum:
         - llm
         - embedding
+        - rerank
       title: ModelType
       description: >-
         Enumeration of supported model types in Llama Stack.
@@ -13585,13 +13486,16 @@ tags:
       embeddings.
 
 
-      This API provides the raw interface to the underlying models. Two kinds of models
+      This API provides the raw interface to the underlying models. Three kinds of
-      are supported:
+      models are supported:
 
       - LLM models: these models generate "raw" and "chat" (conversational) completions.
 
       - Embedding models: these models generate embeddings to be used for semantic
       search.
+
+      - Rerank models: these models reorder the documents based on their relevance
+      to a query.
     x-displayName: Inference
   - name: Inspect
     description: >-

llama_stack/apis/conversations/conversations.py

@@ -4,11 +4,9 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
+from enum import StrEnum
 from typing import Annotated, Literal, Protocol, runtime_checkable
 
-from openai import NOT_GIVEN
-from openai._types import NotGiven
-from openai.types.responses.response_includable import ResponseIncludable
 from pydantic import BaseModel, Field
 
 from llama_stack.apis.agents.openai_responses import (
@@ -150,6 +148,20 @@ class ConversationItemCreateRequest(BaseModel):
     )
 
 
+class ConversationItemInclude(StrEnum):
+    """
+    Specify additional output data to include in the model response.
+    """
+
+    web_search_call_action_sources = "web_search_call.action.sources"
+    code_interpreter_call_outputs = "code_interpreter_call.outputs"
+    computer_call_output_output_image_url = "computer_call_output.output.image_url"
+    file_search_call_results = "file_search_call.results"
+    message_input_image_image_url = "message.input_image.image_url"
+    message_output_text_logprobs = "message.output_text.logprobs"
+    reasoning_encrypted_content = "reasoning.encrypted_content"
+
+
 @json_schema_type
 class ConversationItemList(BaseModel):
     """List of conversation items with pagination."""
@@ -250,13 +262,13 @@ class Conversations(Protocol):
         ...
 
     @webmethod(route="/conversations/{conversation_id}/items", method="GET", level=LLAMA_STACK_API_V1)
-    async def list(
+    async def list_items(
         self,
         conversation_id: str,
-        after: str | NotGiven = NOT_GIVEN,
+        after: str | None = None,
-        include: list[ResponseIncludable] | NotGiven = NOT_GIVEN,
+        include: list[ConversationItemInclude] | None = None,
-        limit: int | NotGiven = NOT_GIVEN,
+        limit: int | None = None,
-        order: Literal["asc", "desc"] | NotGiven = NOT_GIVEN,
+        order: Literal["asc", "desc"] | None = None,
     ) -> ConversationItemList:
         """List items.
 

llama_stack/apis/inference/inference.py

@@ -1234,9 +1234,10 @@ class Inference(InferenceProvider):
 
     Llama Stack Inference API for generating completions, chat completions, and embeddings.
 
-    This API provides the raw interface to the underlying models. Two kinds of models are supported:
+    This API provides the raw interface to the underlying models. Three kinds of models are supported:
     - LLM models: these models generate "raw" and "chat" (conversational) completions.
     - Embedding models: these models generate embeddings to be used for semantic search.
+    - Rerank models: these models reorder the documents based on their relevance to a query.
     """
 
     @webmethod(route="/openai/v1/chat/completions", method="GET", level=LLAMA_STACK_API_V1, deprecated=True)

llama_stack/apis/models/models.py

@@ -27,10 +27,12 @@ class ModelType(StrEnum):
     """Enumeration of supported model types in Llama Stack.
     :cvar llm: Large language model for text generation and completion
     :cvar embedding: Embedding model for converting text to vector representations
+    :cvar rerank: Reranking model for reordering documents based on their relevance to a query
     """
 
     llm = "llm"
     embedding = "embedding"
+    rerank = "rerank"
 
 
 @json_schema_type

llama_stack/core/conversations/conversations.py

@@ -6,9 +6,8 @@
 
 import secrets
 import time
-from typing import Any
+from typing import Any, Literal
 
-from openai import NOT_GIVEN
 from pydantic import BaseModel, TypeAdapter
 
 from llama_stack.apis.conversations.conversations import (
@@ -16,6 +15,7 @@ from llama_stack.apis.conversations.conversations import (
     ConversationDeletedResource,
     ConversationItem,
     ConversationItemDeletedResource,
+    ConversationItemInclude,
     ConversationItemList,
     Conversations,
     Metadata,
@@ -247,7 +247,14 @@ class ConversationServiceImpl(Conversations):
         adapter: TypeAdapter[ConversationItem] = TypeAdapter(ConversationItem)
         return adapter.validate_python(record["item_data"])
 
-    async def list(self, conversation_id: str, after=NOT_GIVEN, include=NOT_GIVEN, limit=NOT_GIVEN, order=NOT_GIVEN):
+    async def list_items(
+        self,
+        conversation_id: str,
+        after: str | None = None,
+        include: list[ConversationItemInclude] | None = None,
+        limit: int | None = None,
+        order: Literal["asc", "desc"] | None = None,
+    ) -> ConversationItemList:
         """List items in the conversation."""
         if not conversation_id:
             raise ValueError(f"Expected a non-empty value for `conversation_id` but received {conversation_id!r}")
@@ -258,14 +265,12 @@ class ConversationServiceImpl(Conversations):
         result = await self.sql_store.fetch_all(table="conversation_items", where={"conversation_id": conversation_id})
         records = result.data
 
-        if order != NOT_GIVEN and order == "asc":
+        if order is not None and order == "asc":
             records.sort(key=lambda x: x["created_at"])
         else:
             records.sort(key=lambda x: x["created_at"], reverse=True)
 
-        actual_limit = 20
+        actual_limit = limit or 20
-        if limit != NOT_GIVEN and isinstance(limit, int):
-            actual_limit = limit
 
         records = records[:actual_limit]
         items = [record["item_data"] for record in records]

llama_stack/core/routers/inference.py

@@ -44,9 +44,14 @@ from llama_stack.apis.inference import (
     OpenAIEmbeddingsResponse,
     OpenAIMessageParam,
     Order,
+    RerankResponse,
     StopReason,
     ToolPromptFormat,
 )
+from llama_stack.apis.inference.inference import (
+    OpenAIChatCompletionContentPartImageParam,
+    OpenAIChatCompletionContentPartTextParam,
+)
 from llama_stack.apis.models import Model, ModelType
 from llama_stack.apis.telemetry import MetricEvent, MetricInResponse, Telemetry
 from llama_stack.log import get_logger
@@ -182,6 +187,23 @@ class InferenceRouter(Inference):
             raise ModelTypeError(model_id, model.model_type, expected_model_type)
         return model
 
+    async def rerank(
+        self,
+        model: str,
+        query: str | OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam,
+        items: list[str | OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam],
+        max_num_results: int | None = None,
+    ) -> RerankResponse:
+        logger.debug(f"InferenceRouter.rerank: {model}")
+        model_obj = await self._get_model(model, ModelType.rerank)
+        provider = await self.routing_table.get_provider_impl(model_obj.identifier)
+        return await provider.rerank(
+            model=model_obj.identifier,
+            query=query,
+            items=items,
+            max_num_results=max_num_results,
+        )
+
     async def openai_completion(
         self,
         params: Annotated[OpenAICompletionRequestWithExtraBody, Body(...)],

llama_stack/core/routers/safety.py

@@ -65,12 +65,16 @@ class SafetyRouter(Safety):
             """Get Shield id from model (provider_resource_id) of shield."""
             list_shields_response = await self.routing_table.list_shields()
 
-            matches = [s.identifier for s in list_shields_response.data if model == s.provider_resource_id]
+            matches: list[str] = [s.identifier for s in list_shields_response.data if model == s.provider_resource_id]
 
             if not matches:
-                raise ValueError(f"No shield associated with provider_resource id {model}")
+                raise ValueError(
+                    f"No shield associated with provider_resource id {model}: choose from {[s.provider_resource_id for s in list_shields_response.data]}"
+                )
             if len(matches) > 1:
-                raise ValueError(f"Multiple shields associated with provider_resource id {model}")
+                raise ValueError(
+                    f"Multiple shields associated with provider_resource id {model}: matched shields {matches}"
+                )
             return matches[0]
 
         shield_id = await get_shield_id(self, model)

llama_stack/log.py

@@ -137,7 +137,8 @@ class CustomRichHandler(RichHandler):
         # Set a reasonable default width for console output, especially when redirected to files
         console_width = int(os.environ.get("LLAMA_STACK_LOG_WIDTH", "120"))
         # Don't force terminal codes to avoid ANSI escape codes in log files
-        kwargs["console"] = Console(width=console_width)
+        # Ensure logs go to stderr, not stdout
+        kwargs["console"] = Console(width=console_width, stderr=True)
         super().__init__(*args, **kwargs)
 
     def emit(self, record):
@@ -177,6 +178,7 @@ def setup_logging(category_levels: dict[str, int] | None = None, log_file: str |
         log_file (str | None): Path to a log file to additionally pipe the logs into.
             If None, reads from LLAMA_STACK_LOG_FILE environment variable.
     """
+    global _category_levels
     # Read from environment variables if not explicitly provided
     if category_levels is None:
         category_levels = dict.fromkeys(CATEGORIES, DEFAULT_LOG_LEVEL)
@@ -184,6 +186,9 @@ def setup_logging(category_levels: dict[str, int] | None = None, log_file: str |
         if env_config:
             category_levels.update(parse_environment_config(env_config))
 
+    # Update the module-level _category_levels so that already-created loggers pick up the new levels
+    _category_levels.update(category_levels)
+
     if log_file is None:
         log_file = os.environ.get("LLAMA_STACK_LOG_FILE")
     log_format = "%(asctime)s %(name)s:%(lineno)d %(category)s: %(message)s"
@@ -268,13 +273,17 @@ def setup_logging(category_levels: dict[str, int] | None = None, log_file: str |
     }
     dictConfig(logging_config)
 
-    # Ensure third-party libraries follow the root log level, but preserve
+    # Update log levels for all loggers that were created before setup_logging was called
-    # already-configured loggers (e.g., uvicorn) and our own llama_stack loggers
     for name, logger in logging.root.manager.loggerDict.items():
         if isinstance(logger, logging.Logger):
-            # Skip infrastructure loggers (uvicorn, fastapi) and our own loggers
+            # Skip infrastructure loggers (uvicorn, fastapi) to preserve their configured levels
-            if name.startswith(("uvicorn", "fastapi", "llama_stack")):
+            if name.startswith(("uvicorn", "fastapi")):
                 continue
+            # Update llama_stack loggers if root level was explicitly set (e.g., via all=CRITICAL)
+            if name.startswith("llama_stack") and "root" in category_levels:
+                logger.setLevel(root_level)
+            # Update third-party library loggers
+            elif not name.startswith("llama_stack"):
                 logger.setLevel(root_level)
 
 

llama_stack/providers/inline/agents/meta_reference/responses/openai_responses.py

@@ -131,7 +131,7 @@ class OpenAIResponsesImpl:
 
             tool_context.recover_tools_from_previous_response(previous_response)
         elif conversation is not None:
-            conversation_items = await self.conversations_api.list(conversation, order="asc")
+            conversation_items = await self.conversations_api.list_items(conversation, order="asc")
 
             # Use stored messages as source of truth (like previous_response.messages)
             stored_messages = await self.responses_store.get_conversation_messages(conversation)

llama_stack/providers/utils/inference/openai_mixin.py

@@ -48,6 +48,7 @@ class OpenAIMixin(NeedsRequestProviderData, ABC, BaseModel):
     - overwrite_completion_id: If True, overwrites the 'id' field in OpenAI responses
     - download_images: If True, downloads images and converts to base64 for providers that require it
     - embedding_model_metadata: A dictionary mapping model IDs to their embedding metadata
+    - construct_model_from_identifier: Method to construct a Model instance corresponding to the given identifier
     - provider_data_api_key_field: Optional field name in provider data to look for API key
     - list_provider_model_ids: Method to list available models from the provider
     - get_extra_client_params: Method to provide extra parameters to the AsyncOpenAI client
@@ -121,6 +122,30 @@ class OpenAIMixin(NeedsRequestProviderData, ABC, BaseModel):
         """
         return {}
 
+    def construct_model_from_identifier(self, identifier: str) -> Model:
+        """
+        Construct a Model instance corresponding to the given identifier
+
+        Child classes can override this to customize model typing/metadata.
+
+        :param identifier: The provider's model identifier
+        :return: A Model instance
+        """
+        if metadata := self.embedding_model_metadata.get(identifier):
+            return Model(
+                provider_id=self.__provider_id__,  # type: ignore[attr-defined]
+                provider_resource_id=identifier,
+                identifier=identifier,
+                model_type=ModelType.embedding,
+                metadata=metadata,
+            )
+        return Model(
+            provider_id=self.__provider_id__,  # type: ignore[attr-defined]
+            provider_resource_id=identifier,
+            identifier=identifier,
+            model_type=ModelType.llm,
+        )
+
     async def list_provider_model_ids(self) -> Iterable[str]:
         """
         List available models from the provider.
@@ -416,21 +441,7 @@ class OpenAIMixin(NeedsRequestProviderData, ABC, BaseModel):
             if self.allowed_models and provider_model_id not in self.allowed_models:
                 logger.info(f"Skipping model {provider_model_id} as it is not in the allowed models list")
                 continue
-            if metadata := self.embedding_model_metadata.get(provider_model_id):
+            model = self.construct_model_from_identifier(provider_model_id)
-                model = Model(
-                    provider_id=self.__provider_id__,  # type: ignore[attr-defined]
-                    provider_resource_id=provider_model_id,
-                    identifier=provider_model_id,
-                    model_type=ModelType.embedding,
-                    metadata=metadata,
-                )
-            else:
-                model = Model(
-                    provider_id=self.__provider_id__,  # type: ignore[attr-defined]
-                    provider_resource_id=provider_model_id,
-                    identifier=provider_model_id,
-                    model_type=ModelType.llm,
-                )
             self._model_cache[provider_model_id] = model
 
         return list(self._model_cache.values())

tests/unit/conversations/test_conversations.py

@@ -82,7 +82,7 @@ async def test_conversation_items(service):
     assert len(item_list.data) == 1
     assert item_list.data[0].id == "msg_test123"
 
-    items = await service.list(conversation.id)
+    items = await service.list_items(conversation.id)
     assert len(items.data) == 1
 
 
@@ -120,7 +120,7 @@ async def test_openai_type_compatibility(service):
         assert hasattr(item_list, attr)
     assert item_list.object == "list"
 
-    items = await service.list(conversation.id)
+    items = await service.list_items(conversation.id)
     item = await service.retrieve(conversation.id, items.data[0].id)
     item_dict = item.model_dump()
 

tests/unit/providers/agents/meta_reference/test_openai_responses_conversations.py

@@ -62,7 +62,7 @@ class TestConversationValidation:
         conv_id = "conv_nonexistent"
 
         # Mock conversation not found
-        mock_conversations_api.list.side_effect = ConversationNotFoundError("conv_nonexistent")
+        mock_conversations_api.list_items.side_effect = ConversationNotFoundError("conv_nonexistent")
 
         with pytest.raises(ConversationNotFoundError):
             await responses_impl_with_conversations.create_openai_response(
@@ -160,7 +160,7 @@ class TestIntegrationWorkflow:
         self, responses_impl_with_conversations, mock_conversations_api
     ):
         """Test creating a response with a valid conversation parameter."""
-        mock_conversations_api.list.return_value = ConversationItemList(
+        mock_conversations_api.list_items.return_value = ConversationItemList(
             data=[], first_id=None, has_more=False, last_id=None, object="list"
         )
 
@@ -227,7 +227,7 @@ class TestIntegrationWorkflow:
         self, responses_impl_with_conversations, mock_conversations_api
     ):
         """Test creating a response with a non-existent conversation."""
-        mock_conversations_api.list.side_effect = ConversationNotFoundError("conv_nonexistent")
+        mock_conversations_api.list_items.side_effect = ConversationNotFoundError("conv_nonexistent")
 
         with pytest.raises(ConversationNotFoundError) as exc_info:
             await responses_impl_with_conversations.create_openai_response(

tests/unit/providers/utils/inference/test_openai_mixin.py

@@ -38,6 +38,28 @@ class OpenAIMixinWithEmbeddingsImpl(OpenAIMixinImpl):
     }
 
 
+class OpenAIMixinWithCustomModelConstruction(OpenAIMixinImpl):
+    """Test implementation that uses construct_model_from_identifier to add rerank models"""
+
+    embedding_model_metadata: dict[str, dict[str, int]] = {
+        "text-embedding-3-small": {"embedding_dimension": 1536, "context_length": 8192},
+        "text-embedding-ada-002": {"embedding_dimension": 1536, "context_length": 8192},
+    }
+
+    # Adds rerank models via construct_model_from_identifier
+    rerank_model_ids: set[str] = {"rerank-model-1", "rerank-model-2"}
+
+    def construct_model_from_identifier(self, identifier: str) -> Model:
+        if identifier in self.rerank_model_ids:
+            return Model(
+                provider_id=self.__provider_id__,  # type: ignore[attr-defined]
+                provider_resource_id=identifier,
+                identifier=identifier,
+                model_type=ModelType.rerank,
+            )
+        return super().construct_model_from_identifier(identifier)
+
+
 @pytest.fixture
 def mixin():
     """Create a test instance of OpenAIMixin with mocked model_store"""
@@ -62,6 +84,13 @@ def mixin_with_embeddings():
     return OpenAIMixinWithEmbeddingsImpl(config=config)
 
 
+@pytest.fixture
+def mixin_with_custom_model_construction():
+    """Create a test instance using custom construct_model_from_identifier"""
+    config = RemoteInferenceProviderConfig()
+    return OpenAIMixinWithCustomModelConstruction(config=config)
+
+
 @pytest.fixture
 def mock_models():
     """Create multiple mock OpenAI model objects"""
@@ -113,6 +142,19 @@ def mock_client_context():
     return _mock_client_context
 
 
+def _assert_models_match_expected(actual_models, expected_models):
+    """Verify the models match expected attributes.
+
+    Args:
+        actual_models: List of models to verify
+        expected_models: Mapping of model identifier to expected attribute values
+    """
+    for identifier, expected_attrs in expected_models.items():
+        model = next(m for m in actual_models if m.identifier == identifier)
+        for attr_name, expected_value in expected_attrs.items():
+            assert getattr(model, attr_name) == expected_value
+
+
 class TestOpenAIMixinListModels:
     """Test cases for the list_models method"""
 
@@ -342,21 +384,71 @@ class TestOpenAIMixinEmbeddingModelMetadata:
             assert result is not None
             assert len(result) == 2
 
-            # Find the models in the result
+            expected_models = {
-            embedding_model = next(m for m in result if m.identifier == "text-embedding-3-small")
+                "text-embedding-3-small": {
-            llm_model = next(m for m in result if m.identifier == "gpt-4")
+                    "model_type": ModelType.embedding,
+                    "metadata": {"embedding_dimension": 1536, "context_length": 8192},
+                    "provider_id": "test-provider",
+                    "provider_resource_id": "text-embedding-3-small",
+                },
+                "gpt-4": {
+                    "model_type": ModelType.llm,
+                    "metadata": {},
+                    "provider_id": "test-provider",
+                    "provider_resource_id": "gpt-4",
+                },
+            }
 
-            # Check embedding model
+            _assert_models_match_expected(result, expected_models)
-            assert embedding_model.model_type == ModelType.embedding
-            assert embedding_model.metadata == {"embedding_dimension": 1536, "context_length": 8192}
-            assert embedding_model.provider_id == "test-provider"
-            assert embedding_model.provider_resource_id == "text-embedding-3-small"
 
-            # Check LLM model
+
-            assert llm_model.model_type == ModelType.llm
+class TestOpenAIMixinCustomModelConstruction:
-            assert llm_model.metadata == {}  # No metadata for LLMs
+    """Test cases for mixed model types (LLM, embedding, rerank) through construct_model_from_identifier"""
-            assert llm_model.provider_id == "test-provider"
+
-            assert llm_model.provider_resource_id == "gpt-4"
+    async def test_mixed_model_types_identification(self, mixin_with_custom_model_construction, mock_client_context):
+        """Test that LLM, embedding, and rerank models are correctly identified with proper types and metadata"""
+        # Create mock models: 1 embedding, 1 rerank, 1 LLM
+        mock_embedding_model = MagicMock(id="text-embedding-3-small")
+        mock_rerank_model = MagicMock(id="rerank-model-1")
+        mock_llm_model = MagicMock(id="gpt-4")
+        mock_models = [mock_embedding_model, mock_rerank_model, mock_llm_model]
+
+        mock_client = MagicMock()
+
+        async def mock_models_list():
+            for model in mock_models:
+                yield model
+
+        mock_client.models.list.return_value = mock_models_list()
+
+        with mock_client_context(mixin_with_custom_model_construction, mock_client):
+            result = await mixin_with_custom_model_construction.list_models()
+
+            assert result is not None
+            assert len(result) == 3
+
+            expected_models = {
+                "text-embedding-3-small": {
+                    "model_type": ModelType.embedding,
+                    "metadata": {"embedding_dimension": 1536, "context_length": 8192},
+                    "provider_id": "test-provider",
+                    "provider_resource_id": "text-embedding-3-small",
+                },
+                "rerank-model-1": {
+                    "model_type": ModelType.rerank,
+                    "metadata": {},
+                    "provider_id": "test-provider",
+                    "provider_resource_id": "rerank-model-1",
+                },
+                "gpt-4": {
+                    "model_type": ModelType.llm,
+                    "metadata": {},
+                    "provider_id": "test-provider",
+                    "provider_resource_id": "gpt-4",
+                },
+            }
+
+            _assert_models_match_expected(result, expected_models)
 
 
 class TestOpenAIMixinAllowedModels:

uv.lock

@@ -2661,7 +2661,7 @@ wheels = [
 
 [[package]]
 name = "openai"
-version = "1.107.0"
+version = "2.5.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "anyio" },
@@ -2673,9 +2673,9 @@ dependencies = [
     { name = "tqdm" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/88/67/d6498de300f83ff57a79cb7aa96ef3bef8d6f070c3ded0f1b5b45442a6bc/openai-1.107.0.tar.gz", hash = "sha256:43e04927584e57d0e9e640ee0077c78baf8150098be96ebd5c512539b6c4e9a4", size = 566056, upload-time = "2025-09-08T19:25:47.604Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/72/39/aa3767c920c217ef56f27e89cbe3aaa43dd6eea3269c95f045c5761b9df1/openai-2.5.0.tar.gz", hash = "sha256:f8fa7611f96886a0f31ac6b97e58bc0ada494b255ee2cfd51c8eb502cfcb4814", size = 590333, upload-time = "2025-10-17T18:14:47.669Z" }
 wheels = [
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